22 citations · 107 across the 37 of their papers we have counts for
4 papers · 1 filter
Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE
Jesun Firoz, Franco Pellegrini, Mario Geiger +17
Chemistry Foundation Models (CFMs) that leverage Graph Neural Networks (GNNs) operating on 3D molecular graph structures are becoming indispensable tools for computational chemists…
BitGNN: Unleashing the Performance Potential of Binary Graph Neural Networks on GPUs
Jou-An Chen, Hsin-Hsuan Sung, Xipeng Shen +2
Recent studies have shown that Binary Graph Neural Networks (GNNs) are promising for saving computations of GNNs through binarized tensors. Prior work, however, mainly focused on a…
Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources
Logan Ward, J. Gregory Pauloski, Valerie Hayot-Sasson +7
Applications that fuse machine learning and simulation can benefit from the use of multiple computing resources, with, for example, simulation codes running on highly parallel supe…
Scalable Triadic Analysis of Large-Scale Graphs: Multi-Core vs. Multi- Processor vs. Multi-Threaded Shared Memory Architectures
George Chin, Andres Marquez, Sutanay Choudhury +1
Triadic analysis encompasses a useful set of graph mining methods that are centered on the concept of a triad, which is a subgraph of three nodes. Such methods are often applied in…